The power of interactive flow in salsa dance: a motion-sensing phenomenological inquiry featuring two-time world champion, Anya Katsevman
Bibliographic record
Abstract
What might it be like to sense one’s motile power as a follower in salsa dance, particularly in moments when flow manifests? Does a follower simply go along with the lead’s flow or does a different kind of flow emerge? Such questions guided this motion-sensing phenomenological (MSP) inquiry into the felt sense of power experienced in the movements of interactive flow that features two-time world salsa champion, coach, and international judge, Anya Katsevman. Over the course of four years, interviews, observations and coaching sessions were analysed through theories purported by dance phenomenologist Maxine Sheets-Johnstone, Daniel Stern, a psychologist who inspired much of Sheets-Johnstone’s writing on the primacy of movement, and the radical phenomenology of Michel Henry who provides a philosophy upon which one may frame the phenomenological ‘search’ for meaning in kinaesthetic terms. The conceptual structure that guided the motion-sensing gathering of data and analysis was the interdisciplinary Function2Flow (F2F) model with its constitutive dimensions of movement Function, Form, Feeling and Flow. As such, the MSP analysis organized in accordance to the F2F model afforded the emergence of micro nuances, detailed physical sensations of this practice, within this macro themed structure. Hence, in detailing the bodily functions and forms of the nuanced gestural communication in salsa dance, with particular attention on the motile sense of power experienced by a follower, a physical pathway to better understanding existential feelings of interactive flow emerged.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".